Career Pathway Analysis System

Web-Based Career Pathway Analysis System Using Academic and Interest Profiling

Student: Michael Gathigi Wanjiru | Reg: IC211-0100/2022
Supervisor: Solomon Mwanjele | Degree: BSc. Information Technology

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QUICK SETUP GUIDE

Prerequisites

• Python 3.12+ installed
• MySQL Server installed and running
• Git (optional)

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STEP 1 — Install Python Dependencies

Open your terminal/command prompt in the project folder:

pip install -r requirements.txt

If mysqlclient fails, try: pip install PyMySQL and add this to career_system/__init__.py:

import pymysql
pymysql.install_as_MySQLdb()

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STEP 2 — Create MySQL Database

Open MySQL terminal or MySQL Workbench and run:

CREATE DATABASE career_pathway_db CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;

Then update career_system/settings.py with your MySQL credentials:

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql',
        'NAME': 'career_pathway_db',
        'USER': 'root',       # your MySQL username
        'PASSWORD': '',       # your MySQL password
        'HOST': 'localhost',
        'PORT': '3306',
    }
}

ALTERNATIVE (SQLite for quick testing):
Comment out the MySQL block in settings.py and uncomment the SQLite block.
No database setup needed for SQLite.

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STEP 3 — Run Database Migrations

python manage.py makemigrations accounts
python manage.py makemigrations core
python manage.py migrate

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STEP 4 — Seed the Database

This loads all career clusters, subjects, weights, RIASEC questions, institutions, courses, and labor market data:

python manage.py seed_data

Expected output:

Starting database seeding...
  Subjects: 21 created
  Career Clusters: 10 created
  Cluster-Subject Weights: 67 created
  RIASEC Questions: 30 created
  Institutions: 25 created
  Courses: 32 created
  Labor Market Data: 10 created
✓ Database seeded successfully!

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STEP 5 — Create Admin Account

python manage.py createsuperuser

Follow the prompts to create an admin username, email, and password.

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STEP 6 — Run the Development Server

python manage.py runserver

Then open your browser and visit:

• Main App: http://127.0.0.1:8000/
• Admin Panel: http://127.0.0.1:8000/admin/

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SYSTEM WORKFLOW

1. Register at /accounts/register/
2. Enter KCSE grades at /grades/
3. Take the RIASEC quiz at /quiz/
4. View results at /results/
5. Download PDF report at /download-report/

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PROJECT FILE STRUCTURE

career_system/
├── manage.py
├── requirements.txt
├── README.md
│
├── career_system/          # Django project config
│   ├── settings.py         # All settings & constants
│   ├── urls.py             # Master URL routing
│   └── wsgi.py
│
├── accounts/               # Authentication app
│   ├── models.py           # StudentUser (custom user model)
│   ├── forms.py            # Registration & login forms
│   ├── views.py            # Register, login, logout, profile
│   ├── urls.py
│   └── admin.py
│
├── core/                   # Main system app
│   ├── models.py           # All 12 database models
│   ├── engine.py           # ★ Recommendation engine (core algorithms)
│   ├── pdf_generator.py    # PDF report generator (ReportLab)
│   ├── views.py            # Dashboard, grades, quiz, results, PDF
│   ├── urls.py
│   ├── admin.py
│   ├── templatetags/
│   │   └── custom_filters.py
│   └── management/
│       └── commands/
│           └── seed_data.py   # Database seeding command
│
└── templates/
    ├── base.html              # Master layout + navbar + footer
    ├── accounts/
    │   ├── register.html
    │   └── login.html
    └── core/
        ├── landing.html       # Public landing page
        ├── dashboard.html     # Student dashboard with progress
        ├── grades_entry.html  # KCSE grade input form
        ├── quiz.html          # 30-question RIASEC quiz
        └── results.html       # Career recommendations + charts

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RECOMMENDATION ENGINE SUMMARY

The engine (core/engine.py) implements three algorithms:

Algorithm 1 — Academic Score (per cluster):

Score(C) = Σ[grade_points × subject_weight] / Σ[12 × subject_weight] × 100

Algorithm 2 — RIASEC Score (per type):

Score(T) = Σ[quiz_responses for type T] / 25 × 100

Algorithm 3 — Interest Score (per cluster):

Interest(C) = Σ[RIASEC_score(T) × affinity_weight(C, T)]

Final Combined Score:

Combined(C) = Academic(C) × 0.60 + Interest(C) × 0.40
             [- 5.0 if cluster is Saturated]

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TESTING

Run the built-in Django tests:

python manage.py test

For manual testing, create a test student via the registration page.

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ADMIN PANEL FEATURES

Access at /admin/ with your superuser credentials:

• View/edit all students and their results
• Manage career clusters, courses, institutions
• Update labor market data
• View RIASEC quiz responses
• Edit subject weights (tune the recommendation engine)

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DATA SOURCES

Data	Source
KCSE Grade Scale	KUCCPS Official Guidelines
Career Clusters	Adapted from KUCCPS Cluster System
Course Requirements	KUCCPS 2023 Booklet
TVET Programs	TVETA Registry
Labor Market Data	KNBS Economic Survey 2023, KEPSA Skills Report
RIASEC Model	Holland (1997) Occupational Theory
Salary Data	Brighter Monday Kenya, KNBS Wage Report

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TECHNOLOGIES USED

Component	Technology	Version
Backend	Python + Django	6.0.3
Database	MySQL	8.0+
PDF Generation	ReportLab	4.4.10
Frontend	Bootstrap 5	5.3.0
Icons	Bootstrap Icons	1.11.0
Charts	Chart.js	4.4.0
Fonts	Google Fonts (Sora + DM Sans)	—

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Final Year Project — Department of Information Technology